import cv2
import numpy as np
import time
import os
import HandTrackingModule as htm

brushThickness = 25
eraserThickness = 100

folderPath = “Header”
myList = os.listdir(folderPath)
print(myList)
overlayList = []
for imPath in myList:
image = cv2.imread(f'{folderPath}/{imPath}’)
overlayList.append(image)
print(len(overlayList))
header = overlayList[0]
drawColor = (255, 0, 255)

cap = cv2.VideoCapture(1)
cap.set(3, 1280)
cap.set(4, 720)

detector = htm.handDetector(detectionCon=0.65,maxHands=1)
xp, yp = 0, 0
imgCanvas = np.zeros((720, 1280, 3), np.uint8)

while True:

# 1. Import image
success, img = cap.read()
img = cv2.flip(img, 1)

# 2. Find Hand Landmarks
img = detector.findHands(img)
lmList = detector.findPosition(img, draw=False)

if len(lmList) != 0:

# print(lmList)

# tip of index and middle fingers
x1, y1 = lmList[8][1:]
x2, y2 = lmList[12][1:]

# 3. Check which fingers are up
fingers = detector.fingersUp()
# print(fingers)

# 4. If Selection Mode – Two finger are up
if fingers[1] and fingers[2]:
# xp, yp = 0, 0
print(“Selection Mode”)
# # Checking for the click
if y1 < 125:
if 250 < x1 < 450:
header = overlayList[0]
drawColor = (255, 0, 255)
elif 550 < x1 < 750:
header = overlayList[1]
drawColor = (255, 0, 0)
elif 800 < x1 < 950:
header = overlayList[2]
drawColor = (0, 255, 0)
elif 1050 < x1 < 1200:
header = overlayList[3]
drawColor = (0, 0, 0)
cv2.rectangle(img, (x1, y1 – 25), (x2, y2 + 25), drawColor, cv2.FILLED)

# 5. If Drawing Mode – Index finger is up
if fingers[1] and fingers[2] == False:
cv2.circle(img, (x1, y1), 15, drawColor, cv2.FILLED)
print(“Drawing Mode”)
if xp == 0 and yp == 0:
xp, yp = x1, y1

cv2.line(img, (xp, yp), (x1, y1), drawColor, brushThickness)

# if drawColor == (0, 0, 0):
# cv2.line(img, (xp, yp), (x1, y1), drawColor, eraserThickness)
# cv2.line(imgCanvas, (xp, yp), (x1, y1), drawColor, eraserThickness)
#
# else:
# cv2.line(img, (xp, yp), (x1, y1), drawColor, brushThickness)
# cv2.line(imgCanvas, (xp, yp), (x1, y1), drawColor, brushThickness)

xp, yp = x1, y1

# # Clear Canvas when all fingers are up
# if all (x >= 1 for x in fingers):
# imgCanvas = np.zeros((720, 1280, 3), np.uint8)

imgGray = cv2.cvtColor(imgCanvas, cv2.COLOR_BGR2GRAY)
_, imgInv = cv2.threshold(imgGray, 50, 255, cv2.THRESH_BINARY_INV)
imgInv = cv2.cvtColor(imgInv,cv2.COLOR_GRAY2BGR)
img = cv2.bitwise_and(img,imgInv)
img = cv2.bitwise_or(img,imgCanvas)

# Setting the header image
img[0:125, 0:1280] = header
# img = cv2.addWeighted(img,0.5,imgCanvas,0.5,0)
cv2.imshow(“Image”, img)
cv2.imshow(“Canvas”, imgCanvas)
cv2.imshow(“Inv”, imgInv)
cv2.waitKey(1)
